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Sentiment classification for insider threat identification using metaheuristic optimized machine learning

Djordje Mladenovic1, Milos Antonijevic2, Luka Jovanovic2

  • 1ICT College of vocational studies, Belgrade, Belgrade, 11000, Serbia.

Scientific Reports
|October 29, 2024
PubMed
Summary

This study enhances organizational security by using Natural Language Processing and Machine Learning to detect insider threats like data breaches. The approach focuses on the context of malicious actions for improved, adaptable threat detection.

Keywords:
AdaBoostHyperparameter optimizationInsider threatNatural language processingXGBoost

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Area of Science:

  • Cybersecurity
  • Data Science
  • Artificial Intelligence

Background:

  • Insider threats pose significant risks, including ransomware, data breaches, and extortion.
  • Traditional security metrics (location, access time) are often insufficient for detecting sophisticated insider threats.
  • Emerging Natural Language Processing (NLP) and Machine Learning (ML) offer new avenues for threat detection.

Purpose of the Study:

  • To develop and evaluate an NLP and ML-based approach for detecting insider threats.
  • To focus on the sentiment and context of malicious actions for more robust threat identification.
  • To introduce a term frequency-inverse document frequency (TF-IDF) based method for enhanced detection.

Main Methods:

  • Utilized six experiments with email, HTTP, and file content data.
  • Employed Natural Language Processing (NLP) techniques with Machine Learning (ML) classifiers (XGBoost, AdaBoost).
  • Implemented a TF-IDF approach and contemporary optimizers, including a modified red fox optimization algorithm, for hyperparameter tuning.

Main Results:

  • The proposed approach demonstrated commendable outcomes in simulated scenarios.
  • The focus on sentiment and context proved effective in identifying malicious actions.
  • The TF-IDF method offered a robust, adaptable, and maintainable detection solution.

Conclusions:

  • NLP and ML techniques, particularly focusing on context and sentiment, are effective in combating insider threats.
  • The TF-IDF approach combined with advanced optimization enhances the adaptability and maintainability of threat detection systems.
  • The study provides a promising framework for improving organizational security against insider risks.